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<h2>Contributors</h2>

<a href="https://martin-danelljan.github.io">Martin Danelljan</a> <br>

<a href="https://www.vision.ee.ethz.ch/en/members/detail/407/">Goutam Bhat</a> <br>
	
<h2>Projects</h2>


<h3><b>CVPR 2019:</b> <a href="https://visionml.github.io/atom/">ATOM: Accurate Tracking by Overlap Maximization</a></h3>
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In this work we primarily address the problem of performing accurate bounding box estimation for generic visual tracking. We train a target estimation module offline, conditioned on the target appearance, to predict the overlap between the object and a bounding box estimate. Furthermore, we propose a target classification component that is learned online using dedicated optimization techniques.
		<br> <b>[<a href="https://visionml.github.io/atom/">Project</a>] [<a href="https://arxiv.org/abs/1811.07628">Paper</a>] [<a href="https://github.com/visionml/pytracking">Code</a>] </b>
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<h3><b>CVPR 2017:</b> <a href="https://visionml.github.io/eco/">ECO: Efficient Convolution Operators for Tracking</a></h3>
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<div>In this work we tackle the key causes behind the problems of computational complexity <i>and</i> over-fitting in advanced DCF trackers. We revisit the core DCF formulation and introduce: (i) a factorized convolution operator, which drastically reduces the number of parameters in the model; (ii) a compact generative model of the training sample distribution, that significantly reduces memory and time complexity, while providing better diversity of samples; (iii) a conservative model update strategy with improved robustness and reduced complexity.
	<br><b>[<a href="https://visionml.github.io/eco/">Project</a>] [<a href="https://arxiv.org/abs/1611.09224">Paper</a>] [<a href="https://github.com/martin-danelljan/ECO">Code</a>]</b>
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